Gray Image extraction using Fuzzy Logic

Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and subsequent extraction from a noise-affected background, with the help of various soft computing methods, are relatively new and quite popular due to various reasons. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods etc. providing an extraction solution working in unsupervised mode happens to be even more interesting problem. Literature suggests that effort in this respect appears to be quite rudimentary. In the present article, we propose a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy Inference System (FIS), Membership Functions, Membership values,Image coding and Processing, Soft Computing, Computer Vision Accepted and published in IEEE. arXiv admin note: text overlap with arXiv:1206.363

Fuzzy Rule Based Enhancement in the SMRT Domain for Low Contrast Images

AbstractFuzzy techniques offer a new and flexible framework for the development of image enhancement algorithms. They are nonlinear, knowledge-based and robust. The potentials of fuzzy set theory for image enhancement are still not investigated in comparison with other established methodologies. In this paper, an examination of fuzzy methods in transform domain is considered. Fuzzy rule based contrast enhancement in the Sequency based Mapped RealTransform (SMRT) domain for block level processing is explored. SMRT, being an integer transform,is computationally efficient and the fuzzy rule based technique is applied to the entire blocks in the transform domain

An Image Compression Scheme Based on Fuzzy Neural Network

Image compression technology is to compress the redundancy between the pixels to reduce the transmission broadband and storage space by using the correlation of the image pixels. Fuzzy neural network effectively integrates neural network technology and fuzzy technology; combines learning, self-adaptivity, imagination and identity and uses rule-based reasoning and fuzzy information processing in the nodes; thus greatly improving the transparency of fuzzy neural network. This paper mainly investigates the applications of fuzzy neural network in image compression and realizes the image compression and reconstruction of fuzzy neural network. It is demonstrated in the simulation experiment that the image compression algorithm based on fuzzy neural network has significant advantages in training speed, compression quality and robustness

A New Approach in a Gray-Level Image Contrast Enhancement by using Fuzzy Logic Technique

Fuzzy Logic technique represents a new approach for gray level image contrast enhancement. The image contrast problem is one of the main problems that confront the researchers in the field of digital image processing, such as in the biomedical image processing like X-Ray and MRI image segmentation for disease classification. In this paper, presenting a new approach to enhancing the image contrast by using fuzzy logic algorithm, so based on the fuzzy rule, we present a new membership equation, which represents the variable threshold level. The proposed method we named it (Fuzzy Hyperbolic Threshold). By using Matlab was implemented the algorithm, and applied to difference gray level images such as old documents images, biomedical images, most of them gives very good results especially with the biomedical images, because of its significant impact on the adjustment of lighting in dark images, clarify its edges, clarify their features and improved image quality

IDENTIFIKASI PENYAKIT ACUTE LYMPHOBLASTIC LEUKEMIA (ALL) MENGGUNAKAN ‘FUZZY RULE-BASED SYSTEM’ BERDASARKAN MORFOLOGI CITRA SEL DARAH PUTIH

IDENTIFIKASI PENYAKIT ACUTE LYMPHOBLASTIC LEUKEMIA (ALL) MENGGUNAKAN ‘FUZZY RULE-BASED SYSTEM’ BERDASARKAN MORFOLOGI CITRA SEL DARAH PUTIH NIZOMJON POLVONOV Jurusan Informatika. Fakultas MIPA. Universitas Sebelas Maret ABSTRAK Prosedur tradisional hitung lengkap sel darah dengan menggunakan mikroskop di Laboratorium Hematologi dilakukan untuk memperoleh Informasi jumlah darah yang lengkap, telah menjadi landasan di laboratorium hematologi untuk mendiagnosis dan memantau gangguan hematologi. Namun, Prosedur tradisional hitung lengkap sel darahmemerlukan tenaga dan waktu yang lama, oleh karena itu cara tes ini merupakan salah satu tes rutin paling mahal di laboratorium klinik hematologi.Untuk mengatasi lamanya waktu pada prosedur yang tradisional WHO merekomendasikan metode Immunophenotyping. Namun immunophenotyping ini masih mempunyai kelemahan, yaitu tidak ada penelusuran sampel sel darah.Upaya untuk mengatasi masalah lamanya waktu dan untuk keperluan penelusuran diagnosa dapat menggunakan teknik pengolahan citra berdasarkan morfologi sel darah. Penelitian ini bertujuan untuk mengidentifikasi Acute Lymphocytic Leukemia (ALL) menggunakan Fuzzy Rule Based System berdasarkan morfologi sel darah putih atau disebut juga White Blood Cell (WBC). Algoritma pengolahan citra yang digunakan adalah thresholding, deteksi tepi canny dan filter warna. Kemudian untuk proses identifikasi presentase sakit ALL digunakan Fuzzy Rule Based Sistem dengan metode Sugeno. Pada proses pengujian digunakan 57 gambar yaitu 35 ALLPositip dan 22 ALL-Negatif. Hasil pengujian menunjukkan akurasi pengujian adalah 73.68% . Kata Kunci: Acute Lymphoblastic Leukemia, Fuzzy Rule-Based System, Granule, Morfologi Sel Darah Putih, Nucleus Ratio, WBC Area. IDENTIFICATION OF ACUTE LYMPHOBLASTIC LEUKEMIA USING ‘FUZZY RULE-BASED SYSTEM’ BASED ON MORPHOLOGICAL CHARACTERISTICS OF WHITE BLOOD CELLS NIZOMJON POLVONOV Department of Imformatics. Mathematic and Science Faculty. Sebelas Maret University ABSTRAKT Over time the information derived from the Complete Blood Count has become cornerstone in laboratory hematology and is widely used for screening, case finding, diagnosis and monitoring hematologic disorders.However the traditional procedure requires effort and a long time, therefore it is one of the most expensive and time consuming routine test in clinical laboratory hematology. To overcome this kind of problem can be used image processing techniques to diagnose diseases based on morphological characteristics of blood cells. This study aims to identify Acute Lymphocytic Leukemia (ALL) using Fuzzy Rule Based System based on morphological characteristics of White Blood Cells (WBC). Image processing algorithms that are used in this study are thresholding, Canny edge detection and color filters. For identification of ALL positive cells Fuzzy Rule Based Systems with Sugeno method is used. For testing process have been used 57 images with 35 ALLPositive 22 and ALL- Negative. The test results showed the accuracy of the test was 73.68%. Keywords: Acute Lymphoblastic Leukemia, Fuzzy Rule-Based System, Granule, Morphology, Nucleus Ratio, WBC Area, White Blood Cell

The VEX-93 environment as a hybrid tool for developing knowledge systems with different problem solving techniques

The paper describes VEX-93 as a hybrid environment for developing knowledge-based and problem solver systems. It integrates methods and techniques from artificial intelligence, image and signal processing and data analysis, which can be mixed. Two hierarchical levels of reasoning contains an intelligent toolbox with one upper strategic inference engine and four lower ones containing specific reasoning models: truth-functional (rule-based), probabilistic (causal networks), fuzzy (rule-based) and case-based (frames). There are image/signal processing-analysis capabilities in the form of programming languages with more than one hundred primitive functions. User-made programs are embeddable within knowledge basis, allowing the combination of perception and reasoning. The data analyzer toolbox contains a collection of numerical classification, pattern recognition and ordination methods, with neural network tools and a data base query language at inference engines's disposal. VEX-93 is an open system able to communicate with external computer programs relevant to a particular application. Metaknowledge can be used for elaborate conclusions, and man-machine interaction includes, besides windows and graphical interfaces, acceptance of voice commands and production of speech output. The system was conceived for real-world applications in general domains, but an example of a concrete medical diagnostic support system at present under completion as a cuban-spanish project is mentioned. Present version of VEX-93 is a huge system composed by about one and half millions of lines of C code and runs in microcomputers under Windows 3.1.Postprint (published version

Fuzzy Rule-based Classification Systems for the Gender Prediction from Handwriting

The handwriting is an object that can describe information about the author implicitly. For example, it is able to predict the gender. Recently, the gender prediction based on handwriting becomes an interesting research. Even in 2013, an competition for prediction gender from handwriting has been held by Kaggle. However, the accuracies of current approaches are relatively low. So, in this study, we attempt to implement Fuzzy Rule-Based Classification Systems (FRBCSs) for gender predictions from handwriting. Three stages are conducted to achieve the objective, as follows: defining some features based on Graphology Techniques (e.g., pressure, height, and margin on writing), collecting real datasets, processing on digital images (i.e., image segmentation, projection profiles, and margin calculation, etc.), and implementing FRBCSs. The implemented algorithm based on FRBCSs in this research is Chi’s Algorithm, which is a method based on Fuzzy Logic for classification tasks. Moreover, some experiments and analysis, involving 75 respondents consisting of 36 males and 39 females, have been done to validate the proposed model. From the simulations, the classification rate obtained is 76%. Besides improving the accuracy rate, the proposed model can provide an understandable model by utilizing fuzzy rule-based systems

Binary operation based hard exudate detection and fuzzy based classification in diabetic retinal fundus images for real time diagnosis applications

Diabetic retinopathy (DR) is one of the most considerable reasons for visual impairment. The main objective of this paper is to automatically detect and recognize DR lesions like hard exudates, as it helps in diagnosing and screening of the disease. Here, binary operation based image processing for detecting lesions and fuzzy logic based extraction of hard exudates on diabetic retinal images are discused. In the initial stage, the binary operations are used to identify the exudates. Similarly, the RGB channel space of the DR image is used to create fuzzy sets and membership functions for extracting the exudates. The membership directives obtained from the fuzzy rule set are used to detect the grade of exudates. In order to evaluate the proposed approach, experiment tests are carriedout on various set of images and the results are verified. From the experiment results, the sensitivity obtained is 98.10%, specificity is 96.96% and accuracy is 98.2%.  These results suggest that the proposed method could be a diagnostic aid for ophthalmologists in the screening for DR